Annotation sources

The annotated variants are based on the following underlying tools and knowledge resources:

  • VEP v85 - Variant Effect Predictor release 85 (GENCODE v19 as the gene reference dataset)
  • COSMIC - catalogue of somatic mutations in cancer (v80 (February 2017))
  • dBNSFP - database of non-synonymous functional predictions (v3.4 (March 2017))
  • ICGC - Somatic mutations discovered in all ICGC tumor cohorts (Release 23 (December 2016))
  • ExAC - germline variant frequencies exome-wide (v1.0 (February 2017))
  • dbSNP - database of short genetic variants (build 147 (April 2016))
  • 1000Genomes - germline variant (phase 3 (20130502))
  • ClinVar - database of clinically related variants (March 2017 (20170302))
  • DoCM - database of curated mutations (v3.2 (April 2016))
  • CIViC - clinical interpretations of variants in cancer (March 17th 2017)
  • CBMDB - Cancer Biomarkers database (February 8th 2017)
  • IntOGen catalog of driver mutations - (2016.05)
  • Cancer Hotspots - a resource for statistically significant mutations in cancer (2016)
  • UniProt/SwissProt KnowledgeBase - resource on protein sequence and functional information (release 2017_03)
  • Pfam - database of protein families and domains (v31.0 (March 2017))
  • DGIdb - database of interactions betweeen antineoplastic drugs and human proteins (v2.22 (February 2016))
  • TSGene - tumor suppressor/oncogene database (v2.0 (2016))
  • KEGG pathways - KEGG pathway database (March 9th 2017)

Somatic SNVs/InDels

Tier 1 - genomic biomarkers for diagnosis, prognosis, and drug sensitivity


Tier 1A biomarkers

The table below lists all variant-evidence item associations:

Tier 1B biomarkers

The table below lists all variant-evidence item associations:

Tier 1C biomarkers

The table below lists all variant-evidence item associations:





Tier 2 - other cancer mutation hotspots, curated mutations or predicted driver mutations

  • A total of 5 unique, somatic variant(s) in the tumor sample are curated as disease-causing, predicted as driver mutations, or occur in previously detected mutation hotspots:



Tier 3 - other coding mutations in proto-oncogenes/tumor suppressors/cancer census genes

  • A total of 218 unique, somatic variant(s) in the tumor sample are found within known proto-oncogenes, tumor suppressor genes or cancer census genes:


Tier 4 - other coding mutations

  • A total of 1994 unique, somatic variant(s) are also found in the tumor sample:





Tier 5 - non-coding mutations

  • A total of 1030 unique, somatic variant(s) are also found in the tumor sample:



Somatic CNA analysis

Segments - amplifications and homozygous deletions




Tumor suppressor genes subject to homozygous deletions





Mutational signatures

The set of somatic mutations observed in a tumor reflects the varied mutational processes that have been active during its life history, providing insights into the routes taken to carcinogenesis. Exogenous mutagens, such as tobacco smoke and ultraviolet light, and endogenous processes, such as APOBEC enzymatic family functional activity or DNA mismatch repair deficiency, result in characteristic patterns of mutation (i.e. distinct patterns of substitution types in specific seqence contexts). Importantly, recent studies show that mutational signatures could have significant clinical impact in certain tumor types (Dong et al. 2016; Secrier et al. 2016; Kim et al. 2016)

Here, we apply the deconstructSigs package (Rosenthal et al. 2016) to delineate the known mutational signatures in a single tumor. This package compares the patterns of mutations observed in a single tumor with a large set of estimated signatures found across tumor types (Alexandrov, Nik-Zainal, Wedge, Campbell, et al. 2013; Alexandrov, Nik-Zainal, Wedge, Aparicio, et al. 2013).

A total of n = 2475 SNVs were used for the mutational signature analysis of this tumor.

Given an input tumor profile and reference input signatures (i.e. 30 mutational signatures detected by Sanger/COSMIC), deconstructSigs iteratively infers the weighted contributions of each reference signature until an empirically chosen error threshold is reached. In the plots below, the top panel is the tumor mutational profile displaying the fraction of mutations found in each trinucleotide context, the middle panel is the reconstructed mutational profile created by multiplying the calculated weights by the signatures, and the bottom panel is the error between the tumor mutational profile and reconstructed mutational profile. The piechart shows the relative contribution of each signature in the sample.



Detected mutational signatures - proposed underlying aetiologies



References

Alexandrov, Ludmil B, Serena Nik-Zainal, David C Wedge, Samuel A J R Aparicio, Sam Behjati, Andrew Biankin V, Graham R Bignell, et al. 2013. “Signatures of Mutational Processes in Human Cancer.” Nature. Nature Publishing Group.

Alexandrov, Ludmil B, Serena Nik-Zainal, David C Wedge, Peter J Campbell, and Michael R Stratton. 2013. “Deciphering Signatures of Mutational Processes Operative in Human Cancer.” Cell Rep. 3 (1): 246–59.

Dong, Fei, Phani K Davineni, Brooke E Howitt, and Andrew H Beck. 2016. “A BRCA1/2 Mutational Signature and Survival in Ovarian High Grade Serous Carcinoma.” Cancer Epidemiol. Biomarkers Prev., 5~aug.

Kim, Jaegil, Kent W Mouw, Paz Polak, Lior Z Braunstein, Atanas Kamburov, Grace Tiao, David J Kwiatkowski, et al. 2016. “Somatic ERCC2 Mutations Are Associated with a Distinct Genomic Signature in Urothelial Tumors.” Nat. Genet. 48 (6): 600–606.

Rosenthal, Rachel, Nicholas McGranahan, Javier Herrero, Barry S Taylor, and Charles Swanton. 2016. “DeconstructSigs: Delineating Mutational Processes in Single Tumors Distinguishes DNA Repair Deficiencies and Patterns of Carcinoma Evolution.” Genome Biol. 17 (1): 31.

Secrier, Maria, Xiaodun Li, Nadeera de Silva, Matthew D Eldridge, Gianmarco Contino, Jan Bornschein, Shona MacRae, et al. 2016. “Mutational Signatures in Esophageal Adenocarcinoma Define Etiologically Distinct Subgroups with Therapeutic Relevance.” Nat. Genet., 5~sep.